SETUP

Import Packages

Import Data

#example grids from package
# bell2010
# mackay1992
# fbb2003

## IMPORT CLEAN PARTICIPANT-LEVEL GRIDS
## load grid (continuous constructs only) for each participant 
## #importExcel from OpenRepGrid package, creates S4 object 

gridType = "short" # use "simple" or "short" where short has even shorter construct names

p4 <-  importExcel(file= paste0("data/participant_grids/", gridType,"/P04_clean.xlsx")) # researcher
p5 <-  importExcel(file= paste0("data/participant_grids/", gridType,"/P05_clean.xlsx")) # researcher
p6 <-  importExcel(file= paste0("data/participant_grids/", gridType,"/P06_clean.xlsx")) # researcher
p7 <-  importExcel(file= paste0("data/participant_grids/", gridType,"/P07_clean.xlsx")) # researcher
p15 <- importExcel(file= paste0("data/participant_grids/", gridType,"/P15_clean.xlsx")) # researcher 


p8 <-  importExcel(file= paste0("data/participant_grids/", gridType,"/P08_clean.xlsx")) # designer
p9 <-  importExcel(file= paste0("data/participant_grids/", gridType,"/P09_clean.xlsx")) # designer
p10 <- importExcel(file= paste0("data/participant_grids/", gridType,"/P10_clean.xlsx")) # designer
p11 <- importExcel(file= paste0("data/participant_grids/", gridType,"/P11_clean.xlsx")) # designer
p12 <- importExcel(file= paste0("data/participant_grids/", gridType,"/P12_clean.xlsx")) # designer
p13 <- importExcel(file= paste0("data/participant_grids/", gridType,"/P13_clean.xlsx")) # designer
p14 <- importExcel(file= paste0("data/participant_grids/", gridType,"/P14_clean.xlsx")) # designer 

## IMPORT RAW CODED-DATA DATAFRAME
## row = one participant construct (elements as cols)
df_raw <- read_csv(file = "data/CODED_CONSTRUCTS.csv",  na=c("", "NA"))

Wrangle Data

names <- c("RESEARCHER P4","RESEARCHER P5","RESEARCHER P6","RESEARCHER P7","RESEARCHER P15","DESIGNER P8","DESIGNER P9","DESIGNER P10","DESIGNER P11","DESIGNER P12","DESIGNER P13","DESIGNER P14")

stimuli <- c("CAREBEAR_BARS","LADY_LINE","BULLET_BARS","CARTOMAP","MAN_INFO","PENGUIN_DISTS","HISTO_DIST",  "IXN_EBARS","IXN_SLOPE","BAYES_RIDGES")



## CREATE GROUP-LEVEL GRIDS
g_researchers <- p4 + p5 + p6 + p7 + p15
g_designers <- p8 + p9 + p10 + p11 + p12 + p13 + p14

## CREATE MASTER GRID
g_all <- p4 + p5 + p6 + p7 + p15 + p8 + p9 + p10 + p11 + p12 + p13 + p14

## CREATE LIST OF GRIDS
list_all <- list(p4 , p5 , p6 , p7 , p15 , p8 , p9 , p10 , p11 , p12 , p13 , p14)


## MINIMAL CODED-DATA FRAME ONLY CONTINUOUS CONSTRUCTS
df_coded <- df_raw %>% 
  filter(CONSTRUCT_TYPE == "NUMERIC") %>% 
  mutate(
    PID = as.factor(PID),
    SAMPLE = factor(SAMPLE),
    CONSTRUCT_TYPE = factor(CONSTRUCT_TYPE),
    POLE_LEFT  = factor(POLE_LEFT),
    POLE_RIGHT = factor(POLE_RIGHT),
    POLES = paste0(POLE_LEFT,"-",POLE_RIGHT),
    FIRST = factor(FIRST),
    SECOND = factor(SECOND),
    THIRD = factor(THIRD),
    CODE_FULL = factor(CODE_STANDARD),
    CODE = factor(paste0(FIRST,"(",SECOND,")")),
    RELFEXIVE = as.logical(REFLEXIVE),
    MATCH = as.logical(MATCH)) %>% 
  mutate(
    across(CAREBEAR_BARS:BAYES_RIDGES, .fns = as.numeric)) %>% 
  select(
    -(CODE_DH:CODE_STANDARD)
  )

## LONG DATAFRAME 
## row = one participant construct X element
df_codedElements <- df_coded %>%
  pivot_longer(
    cols = CAREBEAR_BARS:BAYES_RIDGES,
    names_to ="ELEMENT") %>% 
  mutate(
    value=as.numeric(value),
    POLES = factor(POLES),
    ELEMENT = factor(ELEMENT, levels=stimuli)
  )


## CREATE DF FOR EFA 
## need constructs as columns 
df_eda <- df_codedElements |> 
  pivot_wider(
    names_from=CODE,
    values_from=value
  )
write_csv(df_eda, file = "data/NUMERIC_CONSTRUCTS_WIDE.csv")

PARTICIPANT LEVEL

CORRELATION HEATMAPS

Construct Correlations

## LOOP all participants and render correlation heatmap on constructs
type = "correlation_heatmaps/"
topic = "construct_"
level = "participant/"

i <- 1
for (l in list_all) {
  title <- names[i]
  
  #get correlations
  c <- constructCor(l, trim=50, index=FALSE, method="pearson")

  #render heatmap
  g <- ggcorrplot(c, show.diag = TRUE, show.legend = FALSE, type = "lower",
                  hc.order = FALSE, #hc.ordered using hclust
                  lab = TRUE, lab_size=2.5,
                  tl.cex = 10, tl.srt = 15
    ) + labs(title=paste0(title, " Construct Correlations"))
  print(g)
  
  #save plot
  ggsave(g, filename = paste0("plots/",level, type, topic, title,".png"))
  i <- i + 1
}

#cleanup
rm(g,c)

Element Correlations

## LOOP all participants and render correlation heatmap on elements
type = "correlation_heatmaps/"
topic = "element_"
level = "participant/"

i <- 1
for (l in list_all) {
  title <- names[i]
  
  #get correlations
  c <- elementCor(l, trim=50, index=FALSE, method="pearson")

  #render heatmap
  g <- ggcorrplot(c, show.diag = TRUE, show.legend = FALSE, type = "lower",
                  hc.order = FALSE, #hc.ordered using hclust
                  lab = TRUE, lab_size=2.5,
                  tl.cex = 10, tl.srt = 25
  ) + labs(title=paste0(title, " Element Correlations"))
  print(g)
  
  #save plot
  ggsave(g, filename = paste0("plots/",level, type, topic, title,".png"))
  i <- i + 1
}

#cleanup
rm(g,c)

CLUSTER DENDROGRAMS

Construct Dendrograms

#PRINT CLUSTER FOR EACH PARTICIPANT
# # calculate cluster analysis
# # https://docs.openrepgrid.org/articles/web/clustering.html

type = "cluster_dendrograms/"
topic = "construct_"
level = "participant/"

i <- 1
for (l in list_all) {
  
  # PRINT DENDROGRAM TO FILE
  title <- names[i]
  png(filename=paste0("plots/",level,type,topic,title,".png"), width=10, height = 5, units = "in", res = 300)
  cluster(l, along = 1,#1=constructs, 2 = elements, 0 = both (default)
          dmethod = "euclidean",
          cmethod = "ward.D",
          align = TRUE,
          cex = 0,               # Overall plot text size
          lab.cex = 0.8,           # Label size
          main = title,
          mar = c(2, 1, 2, 25),
          trim=NA)
  # title(main = paste0(title, " - ", "Elements"), adj = 0.05, line=-1, cex.main = 0.85)
  dev.off() #jazz for saving base plots

  
  # PRINT DENDROGRAM TO SCREEN
  cluster(l, along = 1,#1=constructs, 2 = elements, 0 = both (default)
          dmethod = "euclidean",
          cmethod = "ward.D",
          align = TRUE,
          cex = 0,               # Overall plot text size
          lab.cex = 0.8,           # Label size
          main = title,
          mar = c(2, 1, 2, 25),
          trim=NA)
  
  i <- i + 1
}

Element Dendrograms

#PRINT CLUSTER FOR EACH PARTICIPANT
# # calculate cluster analysis
# # https://docs.openrepgrid.org/articles/web/clustering.html

type = "cluster_dendrograms/"
topic = "element_"
level = "participant/"

i <- 1
for (l in list_all) {
  
  # PRINT DENDROGRAM TO FILE
  title <- names[i]
  png(filename=paste0("plots/",level,type,topic,title,".png"),width=10, height = 4, units = "in", res = 300)
  cluster(l, along = 2,#1=constructs, 2 = elements, 0 = both (default)
          dmethod = "euclidean",
          cmethod = "ward.D",
          align = TRUE,
          cex = 0,               # Overall plot text size
          lab.cex = 0.8,           # Label size
          main = title,
          mar = c(2, 1, 2, 25),
          trim=NA)
  # title(main = paste0(title, " - ", "Elements"), adj = 0.05, line=-1, cex.main = 0.85)
  dev.off() #jazz for saving base plots

  
  # PRINT DENDROGRAM TO SCREEN
  cluster(l, along = 2,#1=constructs, 2 = elements, 0 = both (default)
          dmethod = "euclidean",
          cmethod = "ward.D",
          align = TRUE,
          cex = 0,               # Overall plot text size
          lab.cex = 0.8,           # Label size
          main = title,
          mar = c(2, 1, 2, 25),
          trim=NA)
  
  i <- i + 1
}

BERTIN PLOTS

Amy Simple Bertin Plots

## PRINT SIMPLE BERTIN FOR EACH PARTICIPANT
type = "bertin_simpleplots/"
topic = "simpleBertin_"
level = "participant/"

i=1
for (l in list_all){
 
  title = names[i]
  
  png(filename=paste0("plots/",level,type,topic,title,".png"),width=12, height = 4, units = "in", res = 300)

  # https://docs.openrepgrid.org/articles/web/visualization-bertin.html
  # Adjust layout segments: make bertin display area square inside full plot
  bertin(
    l,
    along = 0,
    dmethod = "euclidean",
    cmethod = "ward.D",
    align = TRUE,
    trim = 100,
    main = paste0(title),  # suppress internal title
    colors = c("white", "black"),
    lheight=0.8,
    id = c(TRUE, FALSE)
  )

  # Overlay title in top-left
  #par(fig = c(0, 1, 0.95, 1), mai = c(0, 0, 0, 0), new = TRUE)
  par(fig = c(0, 1, 0.92, 1), mai = c(0, 0, 0, 0), new = TRUE)
  plot.new()
  title(main = title, adj = 0.05, line=-1, cex.main = 0.85)
  
  dev.off() #jazz for saving base plots

  i=i+1
}

print("ONLY SAVED TO FILE")
## [1] "ONLY SAVED TO FILE"

Dana Bertin Cluster Plots

i <- 1
for (l in list_all) {
  title_text <- names[i]

  # Create square plotting device (if saving or in interactive session)
  #par(fig = c(0.1,1,0.1,1), mai = c(0, 0, 0.9, 0.5), new = FALSE)  # more top and right margin
  par(fig = c(0, 1, 0, 1), mai = c(1.2, 4.3, 1.5, 2.5), new = FALSE)
  plot.new()
  #asp <- 1  # aspect ratio

  # Adjust layout segments: make bertin display area square inside full plot
  bertinCluster(
    l,
    along = 0,
    dmethod = "euclidean",
    cmethod = "ward.D",
    align = TRUE,
    type = "rectangle",
    cex = 1,
    lab.cex = 1,
    trim = 50,
    draw.axis = FALSE,
    main = NULL,  # suppress internal title
    colors = c("white", "darkred"),
    lheight=0.75,
    id = c(TRUE, FALSE),
    #xsegs = c(0.0, 0.15, 0.8, 0.91, 1),  # wider center region
    #ysegs = c(0.0, 0.15, 0.8, 1.0)       # to match horizontal size
    xsegs = c(0.0, 0.17, 0.77, 0.91, 1),  # allow a little wider middle block
    ysegs = c(0.0, 0.14, 0.8, 1.0)        # more space to the matrix vertically
  )

  # Overlay title in top-left
  #par(fig = c(0, 1, 0.95, 1), mai = c(0, 0, 0, 0), new = TRUE)
  par(fig = c(0, 1, 0.92, 1), mai = c(0, 0, 0, 0), new = TRUE)
  plot.new()
  title(main = title_text, adj = 0.2, line=-1, cex.main = 0.85)

  i <- i + 1
}

Dana Bertin Cluster Plots 2

## TODO WRITE SIMPLE BERTIN LOOP 
i=1
for (l in list_all){
   par(fig = c(0,1,0,1), mai = c(0.2, 0.1, 0.1, 0.2), new = FALSE)  # more top and right margin
  plot.new()
 
title = names[i]
clustered_elements <- cluster(l,
                    along = 2, #1=constructs, 2 = elements, 0 = both (default)
                    dmethod =  "euclidean",#distance measure TODO evaluate diff options
                    cmethod="ward.D", #agglomeration method TODO evaluate diff option
                    cex = 1, lab.cex = 1, main = paste0(title, " - ", "Elements")
                    )

clustered_constructs <- cluster(l,
                    along = 1, #1=constructs, 2 = elements, 0 = both (default)
                    dmethod =  "euclidean",#distance measure TODO evaluate diff options
                    cmethod="ward.D", #agglomeration method TODO evaluate diff option
                    cex = 1, lab.cex = 1, main = paste0(title, " - ", "Constructs")
                    )

clustered <- cluster(l,
                    along = 0, #1=constructs, 2 = elements, 0 = both (default)
                    dmethod =  "euclidean",#distance measure TODO evaluate diff options
                    cmethod="ward.D", #agglomeration method TODO evaluate diff option
                    cex = 1, lab.cex = 1, main = paste0(title, " - ", "Both")
                    )

  # https://docs.openrepgrid.org/articles/web/visualization-bertin.html
#bertin(clustered,
        #trim=50, draw.axis = TRUE,
        #colors = c("white", "darkred"))
  
#op <- par(fig = c(0,1,0.1,1), mai=c(0,0,0.2,0), cex.main=0.85, adj=0, new = TRUE)
  #title(title)

  # Create square plotting device (if saving or in interactive session)
  #par(fig = c(0.1,1,0.1,1), mai = c(0, 0, 0.2, 0), new = TRUE)  # more top and right margin
  #par(fig = c(0, 1, 0, 1), mai = c(1.2, 4.2, 1.5, 2.5), new = FALSE)
  #plot.new()
  #asp <- 1  # aspect ratio

  # Adjust layout segments: make bertin display area square inside full plot
  bertin(
    clustered,
    along = 0,
    dmethod = "euclidean",
    cmethod = "ward.D",
    align = TRUE,
    trim = 50,
    main = paste0(title),  # suppress internal title
    colors = c("white", "darkred"),
    lheight=0.8,
    id = c(TRUE, FALSE)
  )

  # Overlay title in top-left
  #par(fig = c(0, 1, 0.95, 1), mai = c(0, 0, 0, 0), new = TRUE)
  par(fig = c(0, 1, 0.92, 1), mai = c(0, 0, 0, 0), new = TRUE)
  plot.new()
  title(main = title, adj = 0.05, line=-1, cex.main = 0.85)

  i<-i+1
}

PCA BIPLOTS

## PRINT PCA BIPLOT for each participant

type = "pca_biplots/"
topic = "biplot2d_"
level = "participant/"

i=1
for (l in list_all){
  
  ## PRINT BIPLOT TO FILE
  title = names[i]
  png(filename=paste0("plots/",level,type,topic,title,".png"),width=8, height = 8, units = "in", res = 300)
  # https://docs.openrepgrid.org/articles/web/visualization-biplot.html
  biplot2d(l, 
           dim = c(2,1),
           zoom = 1,
      
           ## construct s
           c.lines = TRUE,
           col.c.lines= gray(0.9),
           c.label.cex = 0.5,
           c.labels.inside = FALSE,
           c.label.col = "blue",
           
           ## elements
           # rect.margins = c(2,2),
           e.point.col = "red",
           e.label.col = "red",
           e.label.cex = 0.5, #element label size
           
           
           ## size and margins 
           mai = c(0.2,1.5,.2,1.5),
           unity=TRUE, #just makes it neater
           scale.e = 0.75, 
          
           )
  op <- par(# fig = c(0,1,0.5,1), 
           cex.main = 0.75, #title size
           new = TRUE)
  title(title)
  dev.off()
  
  
  ## PRINT BIPLOT TO SCREEN
  # https://docs.openrepgrid.org/articles/web/visualization-biplot.html
  biplot2d(l, 
           dim = c(2,1),
           zoom = 1,
      
           ## construct s
           c.lines = TRUE,
           col.c.lines= gray(0.9),
           c.label.cex = 0.5,
           c.labels.inside = FALSE,
           c.label.col = "blue",
           
           ## elements
           # rect.margins = c(2,2),
           e.point.col = "red",
           e.label.col = "red",
           e.label.cex = 0.5, #element label size
           
           
           ## size and margins 
           mai = c(0.2,1.5,.2,1.5),
           unity=TRUE, #just makes it neater
           scale.e = 0.75, 
          
           )
  op <- par(# fig = c(0,1,0.5,1), 
           cex.main = 0.75, #title size
           new = TRUE)
  title(title)
  
  
  i=i+1
}

WIP GROUP LEVEL

COMPARE GROUPS - DENDROGRAM CONSTRUCTS

 cluster(g_researchers, along = 1, #1=constructs, 2 = elements, 0 = both (default)
        trim=100,
         dmethod =  "euclidean",#distance measure TODO evaluate diff options    
        cmethod="ward.D", #agglomeration method TODO evaluate diff options
        align = TRUE, #align b4 clustering? reverses constructs if necessary to yield maximal simmilarity
        cex = 1, lab.cex = 1, main = title)  

cluster(g_designers, along = 1, #1=constructs, 2 = elements, 0 = both (default)
       trim=100,
         dmethod =  "euclidean",#distance measure TODO evaluate diff options    
        cmethod="ward.D", #agglomeration method TODO evaluate diff options
        align = TRUE, #align b4 clustering? reverses constructs if necessary to yield maximal simmilarity
        cex = 1, lab.cex = 1, main = title)

COMPARE GROUPS - DENDROGRAM ELEMENTS

 cluster(g_researchers, along = 2, #1=constructs, 2 = elements, 0 = both (default)
        trim=100,
         dmethod =  "euclidean",#distance measure TODO evaluate diff options    
        cmethod="ward.D", #agglomeration method TODO evaluate diff options
        align = TRUE, #align b4 clustering? reverses constructs if necessary to yield maximal simmilarity
        cex = 1, lab.cex = 1, main = "RESEARCHERS")  

cluster(g_designers, along = 2, #1=constructs, 2 = elements, 0 = both (default)
       trim=100,
         dmethod =  "euclidean",#distance measure TODO evaluate diff options    
        cmethod="ward.D", #agglomeration method TODO evaluate diff options
        align = TRUE, #align b4 clustering? reverses constructs if necessary to yield maximal simmilarity
        cex = 1, lab.cex = 1, main = "DESIGNERS")

COMPARE GROUPS — PCA/BIPLOTS

  ## RESEARCHER BIPLOT
  title = "RESEARCHERS"
  print(title)
## [1] "RESEARCHERS"
  biplot2d(g_researchers,
           dim = c(2,1),
           zoom = 1,
      
           ## construct s
           c.lines = TRUE,
           col.c.lines= gray(0.9),
           c.label.cex = 0.5,
           c.labels.inside = FALSE,
           c.label.col = "blue",
           
           ## elements
           # rect.margins = c(2,2),
           e.point.col = "red",
           e.label.col = "red",
           e.label.cex = 0.5, #element label size
           
           
           ## size and margins 
           mai = c(0.2,1.5,.2,1.5),
           unity=TRUE, #just makes it neater
           scale.e = 0.75, 
          
           )
  op <- par(# fig = c(0,1,0.5,1), 
           cex.main = 0.75, #title size
           new = TRUE)
  title(title)

  ## DESIGNER BIPLOT
  title = "DESIGNERS"
  print(title)
## [1] "DESIGNERS"
  biplot2d(g_designers,
           dim = c(2,1),
           zoom = 1,
      
           ## construct s
           c.lines = TRUE,
           col.c.lines= gray(0.9),
           c.label.cex = 0.5,
           c.labels.inside = FALSE,
           c.label.col = "blue",
           
           ## elements
           # rect.margins = c(2,2),
           e.point.col = "red",
           e.label.col = "red",
           e.label.cex = 0.5, #element label size
           
           
           ## size and margins 
           mai = c(0.2,1.5,.2,1.5), #margins 
           unity=TRUE, #just makes it neater
           scale.e = 0.75, 
          
           )
  op <- par(# fig = c(0,1,0.5,1), 
           cex.main = 0.75, #title size
           new = TRUE)
  title(title)

ARF WIP

WIP PCA — CONSTRUCTS

###### OPEN REP GRID APPROACH
constructPca(p15, nfactors = 2, trim=50, rotate="varimax",method="pearson")
## 
## #################
## PCA of constructs
## #################
## 
## Number of components extracted: 2
## Type of rotation: varimax 
## 
## Loadings:
##                                       RC1   RC2  
## context-dependent - stand-alone        0.59 -0.58
## quick to make - long time to make      0.90  0.18
## sole author - team author              0.87 -0.15
## author no stats backgroun - stats phd  0.08  0.92
## story-first - data-first              -0.17  0.86
## hate (rip up) - love (publish)         0.76 -0.40
## not clever - clever                    0.55 -0.61
## 
##                 RC1  RC2
## SS loadings    2.84 2.51
## Proportion Var 0.41 0.36
## Cumulative Var 0.41 0.76
###### VERSION psych::principal() 
## constructPca() is equivalent to this
corr <- constructCor(p15)

(p <- principal(corr, nfactors = 2, rotate="varimax", cor = "cor")) 
## Principal Components Analysis
## Call: principal(r = corr, nfactors = 2, rotate = "varimax", cor = "cor")
## Standardized loadings (pattern matrix) based upon correlation matrix
##                           RC1   RC2   h2   u2 com
## context-de - stand-alon  0.59 -0.58 0.69 0.31 2.0
## quick to m - long time   0.90  0.18 0.84 0.16 1.1
## sole autho - team autho  0.87 -0.15 0.78 0.22 1.1
## author no  - stats phd   0.08  0.92 0.85 0.15 1.0
## story-firs - data-first -0.17  0.86 0.77 0.23 1.1
## hate (rip  - love (publ  0.76 -0.40 0.75 0.25 1.5
## not clever - clever      0.55 -0.61 0.68 0.32 2.0
## 
##                        RC1  RC2
## SS loadings           2.84 2.51
## Proportion Var        0.41 0.36
## Cumulative Var        0.41 0.76
## Proportion Explained  0.53 0.47
## Cumulative Proportion 0.53 1.00
## 
## Mean item complexity =  1.4
## Test of the hypothesis that 2 components are sufficient.
## 
## The root mean square of the residuals (RMSR) is  0.11 
## 
## Fit based upon off diagonal values = 0.95
print(p)## PREFERRED OUTPUT! 
## Principal Components Analysis
## Call: principal(r = corr, nfactors = 2, rotate = "varimax", cor = "cor")
## Standardized loadings (pattern matrix) based upon correlation matrix
##                           RC1   RC2   h2   u2 com
## context-de - stand-alon  0.59 -0.58 0.69 0.31 2.0
## quick to m - long time   0.90  0.18 0.84 0.16 1.1
## sole autho - team autho  0.87 -0.15 0.78 0.22 1.1
## author no  - stats phd   0.08  0.92 0.85 0.15 1.0
## story-firs - data-first -0.17  0.86 0.77 0.23 1.1
## hate (rip  - love (publ  0.76 -0.40 0.75 0.25 1.5
## not clever - clever      0.55 -0.61 0.68 0.32 2.0
## 
##                        RC1  RC2
## SS loadings           2.84 2.51
## Proportion Var        0.41 0.36
## Cumulative Var        0.41 0.76
## Proportion Explained  0.53 0.47
## Cumulative Proportion 0.53 1.00
## 
## Mean item complexity =  1.4
## Test of the hypothesis that 2 components are sufficient.
## 
## The root mean square of the residuals (RMSR) is  0.11 
## 
## Fit based upon off diagonal values = 0.95
############ ?? not really sure if this is element or construct?
###### VERSION base::prcomp() 
### 1. CREATE TRANSPOSED DF FOR PCA ON CONSTRUCTS
df <- df_coded %>% 
  filter(PID=="P15") %>%
  select(CAREBEAR_BARS:BAYES_RIDGES, POLES) 
poles <- df$POLES # save construct names
#transpose
df <- t(df) %>% as_tibble()
## Warning: The `x` argument of `as_tibble.matrix()` must have unique column names if
## `.name_repair` is omitted as of tibble 2.0.0.
## ℹ Using compatibility `.name_repair`.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
colnames(df) = poles
#drop last row
df <- df[1:length(stimuli),]  # %>% slice(1:(n() - 1))
df <- df %>% mutate_all(as.numeric)
df <- df %>% mutate(element = factor(stimuli))
dpca <- df %>% select(where(is.numeric)) #get just the numeric cols 

### 2. RUN PCA ON DT 
pca <- prcomp(dpca, scale = TRUE )
summary(pca)
## Importance of components:
##                           PC1    PC2    PC3     PC4     PC5     PC6     PC7
## Standard deviation     1.9293 1.2776 0.9003 0.67565 0.44771 0.37758 0.18806
## Proportion of Variance 0.5318 0.2332 0.1158 0.06521 0.02864 0.02037 0.00505
## Cumulative Proportion  0.5318 0.7649 0.8807 0.94595 0.97458 0.99495 1.00000
# data plot
autoplot(pca, data = df, color = "element", label=TRUE, label.size=5) + theme_minimal()

# biplot
biplot(pca)

# scree plot
plot(pca, type="lines")

WIP PCA ELEMENTS

ALL PARTICIPANTS

Code Frequency table

## TABLE AT FIRST
crosstab(data = df_codedElements, rowvar = FIRST, colvar = SAMPLE, type = "percent")   # or "prop.col", 
##           SAMPLE
## FIRST      DESIGNER RESEARCHER   Total
##   artifact   34.41%     15.05%  49.46%
##   audience    5.38%      5.38%  10.75%
##   data        1.08%      3.23%   4.30%
##   maker      18.28%     16.13%  34.41%
##   mode        1.08%      0.00%   1.08%
##   Total      60.22%     39.78% 100.00%
## DATAFRAME
df <- df_codedElements %>% 
  select(FIRST,SECOND,THIRD,POLES,CODE_FULL,ELEMENT,SAMPLE,PID) 

## TWO LEVEL TABLE 
# one row per participant element X code
table_df <- df %>%
  count(FIRST, SECOND, SAMPLE) %>%
  pivot_wider(names_from = SAMPLE, values_from = n, values_fill = 0) %>%
  arrange(FIRST, SECOND) %>%
  group_by(FIRST) %>%
  mutate(FIRST = if_else(row_number() == 1, FIRST, ""),
         DESIGNER=DESIGNER/10,
         RESEARCHER=RESEARCHER/10
         ) %>%
  ungroup()
table_df %>% gt()
FIRST SECOND DESIGNER RESEARCHER
artifact behavior 14 5
design 4 5
genre 0 1
intent 5 1
register 0 1
trust 3 1
usability 6 0
audience behavior 0 1
skill 2 3
type 3 1
data source 1 0
topic 0 1
trust 0 2
maker dilligence 4 4
intent 2 1
skill 11 7
trust 0 2
type 0 1
mode type 1 0
 
## THREE LEVEL TABLE
table_df <- df %>%
  count(FIRST, SECOND, THIRD, SAMPLE) %>%
  pivot_wider(
    names_from = SAMPLE,
    values_from = n,
    values_fill = 0
  ) %>%
  arrange(FIRST, SECOND, THIRD) %>%
  group_by(FIRST, SECOND) %>%
  mutate(
    THIRD = as.character(THIRD),
    SECOND = if_else(row_number() == 1, SECOND, ""),
    FIRST = if_else(row_number() == 1, FIRST, ""),
    DESIGNER=DESIGNER/10,
    RESEARCHER=RESEARCHER/10
  ) %>%
  ungroup()
table_df %>% gt()
FIRST SECOND THIRD DESIGNER RESEARCHER
artifact behavior commenting 1 0
encounter 7 4
infoseek 2 0
like/upvote 2 0
redesign 0 1
save 1 0
sharing 1 0
artifact design appeal 1 0
appropriateness 1 0
clever 0 1
duration 0 1
encoding 1 0
like 0 1
quality 1 0
salience 0 1
vibe 0 1
artifact genre completeness 0 1
artifact intent data 1 0
narrative 1 0
NA 3 1
artifact register NA 0 1
artifact trust NA 3 1
artifact usability accessibility 1 0
clarity 1 0
effort 2 0
legibility 1 0
readability 1 0
audience behavior encounter 0 1
audience skill dataliteracy 1 1
statistics 1 0
NA 0 2
audience type NA 3 1
data source NA 1 0
data topic interest 0 1
data trust NA 0 2
maker dilligence data 0 1
design 2 1
storytelling 1 0
NA 1 2
maker intent NA 2 1
maker skill communication 4 1
data 2 0
design 3 3
statistics 1 2
visualization 1 0
NA 0 1
maker trust NA 0 2
maker type group size 0 1
mode type NA 1 0
 
## CONSTRUCT  LEVEL TABLE
table_df <- df %>%
  group_by(FIRST, SECOND, THIRD, SAMPLE) %>%
  summarise(POLES_values = paste(unique(POLES), collapse = ","), .groups = "drop") %>%
  pivot_wider(
    names_from = SAMPLE,
    values_from = POLES_values,
    values_fill = ""
  ) %>%
  arrange(FIRST, SECOND, THIRD) %>%
  group_by(FIRST, SECOND) %>%
  mutate(
    THIRD = as.character(THIRD),
    SECOND = if_else(row_number() == 1, SECOND, ""),
    FIRST = if_else(row_number() == 1, FIRST, "")
  ) %>%
  ungroup()
table_df %>% gt()
FIRST SECOND THIRD DESIGNER RESEARCHER
artifact behavior commenting will not comment-Leave a comment
encounter low engagement (scroll past)-high engagement (stop and look),would not spend time looking at-would spend time looking at,scroll past-stop and look,keep scrolling-stop and look ,scroll on-pause,scroll on-stop and look stop-scroll,ignore-stop and look,participant would not want to look-participant would want to look,how long to look at chart (in seconds)-NA
infoseek would not read contextual information-would read more contextual information,less likely to read contextual information-more likely to read
like/upvote less likely to “like” react-more likely to “like”,will not upvote-will upvote
redesign would not want to change based on context-would want to change
save less likely to save-more likely to save
sharing will not share-Share it with someone
artifact design appeal very ugly (personal preference)-very nice
appropriateness bad data representation-good data representation
clever not clever-clever
duration quick to make-long time to make
encoding color as decorative or nonexistent-color as a visual encoding
like hate (rip up)-love (publish)
quality lower design quality-higher design quality
salience visualization design not interesting-visualization design interesting
vibe dry-sensational
artifact genre completeness context-dependent-stand-alone
artifact intent data Not showcasing the data-Showcasing the data
narrative Not tell a story with data-Tell a story with data (call for action)
NA more illustrative focus-more data chart type focus,explanatory-call for action (want to change behavior),informative-narrative data serving story (story first)-story serving data (data first/story ad hoc)
artifact register NA informal-formal
artifact trust NA not trustworthy-trustworthy,Untrustworthy-Trustworthy,misleading-unbiased not biased-biased
artifact usability accessibility low accessibility to visually impaired readers-high accessibility to visually impaired readers
clarity not clear overall (hard to understand)-clear overall (easy to understand)
effort easier to understand-harder to understand,hard to understand-easy to understand
legibility not legible (no idea what it’s writing)-legible (I can read it!)
readability not readable (no idea about the message)-readable (I can understand it!)
audience behavior encounter target audience would not want to look-target audience want to look at it
audience skill dataliteracy Audience low data literacy-Audience high data literacy viewer no training in data visualization-viewer training in data visualization
statistics audience low stats skills-audience high stats skills
NA audience education level (low)-high edu level,less educated audience-more educated audience
audience type NA Niche audience-General audience,niche audience-general audience,specific audience-general audience general audience-niche audience
data source NA Data collector and data designer are different people-Data collector and data designer are the same person
data topic interest topic not interesting-topic interesting
data trust NA don't trust the data-trust the data
maker dilligence data does not care about data-care about data
design not much thought put into chart type-thought put into chart type,less thought put into designing-more thought put into designing does not care about design-care about design
storytelling not much thought put into storytelling-thought put into storytelling
NA less time spent designing (labor)-more time spent designing (labor) no skin in the game (no thought put into making the design of the viz)-skin in the game (more thought put into making the design),author did not try-author tried
maker intent NA More informative-More call-to-action, pure information-author has an intention (biased) informative-persuasive
maker skill communication author low communication skills-author high communication skills,Poorly communicated-Communicated,less successful at conveying message-more successful at conveying message,low visual communication skills-high visual communication skills author not effective in conveying message-author effective in conveying message
data author low data-analysis skills-high data-analysis skills,author less data analysis skills-author more data analysis skills
design designer has less design skills-designer has more design skills,author less data communication (design) skills-author more data communication (design) skills,Design quality - bad-Design quality - Good not professional designers-professional designers,author has less design skills-more design skills,author's design skills (low)-(high)
statistics author low stats skills-author high stats skills author has less statistics skills-more statistics skills,author no stats background-stats phd
visualization author low data-viz skills-high data-viz skills
NA low author competancy-high author competancy
maker trust NA don't believe the author's message-believe author's message,don't turst the author-trust the author
maker type group size likelihood of made by one person (3 means unsure)-probably made by multiple people
mode type NA Publication source (Reddit) (3 = new york post) -publication source
(Jstor) 
###### CODE LEVEL TO XLS
table_df <- df %>%
  group_by(FIRST, SECOND, THIRD, SAMPLE) %>%
  summarise(POLES_values = paste(unique(POLES), collapse = "\n"), .groups = "drop") %>%
  pivot_wider(
    names_from = SAMPLE,
    values_from = POLES_values,
    values_fill = ""
  ) %>%
  arrange(FIRST, SECOND, THIRD) %>%
  group_by(FIRST, SECOND) %>%
  mutate(
    THIRD = as.character(THIRD),
    SECOND = if_else(row_number() == 1, SECOND, ""),
    FIRST = if_else(row_number() == 1, FIRST, "")
  ) %>%
  ungroup()
# table_df
# knitr::kable(table_df)
# library(gt)
# table_df %>% gt()

write.xlsx(table_df, file = "table.xlsx", colNames=TRUE, asTable = TRUE)

!! WIP LMER

# df <- df_codedElements %>% 
#   select(
#     value, ELEMENT,CODE,POLES,PID,SAMPLE
#   )
# 
# 
# 
# m0 <- lmer(value ~ (1|PID),  data = df )
# summary(m0)
# 
# levels(df$ELEMENT)
# m1 <- lmer(value ~ ELEMENT*CODE + SAMPLE + (1|PID), data = df)
# summary(m1)
# plot_model(m1,type="pred", terms=c("CODE"))

FUTURE AMY TODO

Conflicts

## TODO WTAF is measured as 'conflict'? see 
# https://docs.openrepgrid.org/articles/web/measures-conflict.html
#Bell, R. C. (2004). A new approach to measuring inconsistency or conflict in grids. Personal Construct Theory & Practice, 1, 53–59.
#Heider, F. (1946). Attitudes and cognitive organization. Journal of Psychology, 21, 107–112.

indexConflict3(p4)
## 
## ##########################################################
## CONFLICT OR INCONSISTENCIES BASED ON TRIANGLE INEQUALITIES
## ##########################################################
## 
## Potential conflicts in grid:  150
## Actual conflicts in grid:  66
## Overall percentage of conflict in grid:  44 %
## 
## ELEMENTS
## ########
## 
## Percent of conflict attributable to element:
## 
##                 percentage
## 1 CAREBEAR_BARS    1666.67
## 2 LADY_LINE        1363.64
## 3 BULLET_BARS      1212.12
## 4 CARTOMAP          454.55
## 5 TAXMAN_INFO       151.52
## 6 PENGUIN_DISTS     606.06
## 7 HISTO_DIST       1666.67
## 8 IXN_EBARS         909.09
## 9 IXN_SLOPE         909.09
## 10 BAYES_RIDGES    1060.61
## 
## Chi-square test of equal count of conflicts for elements.
## 
##  Chi-squared test for given probabilities
## 
## data:  x$e.count
## X-squared = 14.909, df = 9, p-value = 0.09346
## 
## 
## CONSTRUCTS
## ##########
## 
## Percent of conflict attributable to construct:
## 
##                           percentage
## 1 not biased - biased          16.67
## 2 dry - sensationa             14.39
## 3 don't trus - trust data      12.88
## 4 don't beli - believe au      17.42
## 5 low audien - high edu l      14.39
## 6 stop - scroll                24.24
## 
## Chi-square test of equal count of conflicts for constructs.
## 
##  Chi-squared test for given probabilities
## 
## data:  x$c.count
## X-squared = 6.5455, df = 5, p-value = 0.2567

Implicative Dilema

DECIDED to only ever consider this at the individual level b/c whether the same construct is valenced, and toward what pole can meaningfully differ between participants thus complicating / negating any group-level analysis.

#https://docs.openrepgrid.org/articles/web/measures-implicative.html

# Implicative dilemmas are closely related to the notion of conflict. An implicative dilemma arises when a desired change on one construct is associated with an undesired implication on another construct. E. g. a timid subject may want to become more socially skilled but associates being socially skilled with different negative characteristics (selfish, insensitive etc.). Hence, he may anticipate that becoming less timid will also make him more selfish (cf. Winter, 1982). 

i <- indexDilemma(p15, self=, ideal=10)
## TODO really actually figure out 1. if this is useful and 2. what it is doing. 3. how to define the self (vs) ideal self and align poles
plot(i)

SCRATCH

Create Grid from dataframe

# # CREATE a custom grid from the coded constructs dataframe
# 
# ######## FILTER MASTER DATAFRAME 
# d <- df %>% 
#   filter(
#       PID=="P15",
#       CONSTRUCT_TYPE=="NUMERIC"
#       ) %>% 
#   mutate_at(vars(CAREBEAR_BARS:BAYES_RIDGES), as.numeric) %>% 
#   mutate(
#     COUPLED = paste0(POLE_LEFT,"-",POLE_RIGHT),
#     CONSTRUCT = paste0(FIRST,"(",SECOND,")")
#   ) %>% select (
#     POLE_LEFT, POLE_RIGHT,
#     COUPLED,
#     CONSTRUCT,
#     CAREBEAR_BARS:BAYES_RIDGES)
#   # ) %>% column_to_rownames(var = "CONSTRUCT")
#   # ) %>% column_to_rownames(var = "CODE_STANDARD")
# ########### 
# 
# ## elements
# e <- d %>% select(-(POLE_LEFT:CONSTRUCT)) %>% colnames()
# # e <- c("care-bear","diamond-lady","bullets","heatmap","taxes",
#        # "penguins","physics-matplotlib","interaction","slope-interaction","bayesian")
# ## construct left pole
# l <- d %>% pull(POLE_LEFT)
# ## construct right pole
# r <- d %>% pull(POLE_RIGHT)
# ## construct code
# c <- d %>% pull(CONSTRUCT)
# 
# ## ratings
# ## have to unravel dataframe by row; unlist goes by column, 
# ## so instead, first transpose, then use 
# s <- c(t(d %>% select(CAREBEAR_BARS:BAYES_RIDGES)))
# 
# ## ASSEMBLE NEW REPGRID OBJECT
# ## args 
# args <- list(
#   name = e,
#   l.name = c,
#   # r.name = r,
#   coupled =F,
#   scores = s
#   ) 
# t15 <- makeRepgrid(args)
# t15 <- setScale(t15, 1, 5)
# t15
# 
# 
# g_double <- t15+t15

Construct Description (RAW)

statsConstructs(p15,trim=50)
## 
## ####################################
## Desriptive statistics for constructs
## ####################################
## 
##                                           vars  n mean   sd median trimmed  mad
## (1) context-dependent - stand-alone          1 10 3.35 1.73   4.00    3.44 1.48
## (2) quick to make - long time to make        2 10 3.00 1.68   3.00    3.00 2.59
## (3) sole author - team author                3 10 2.50 1.78   1.50    2.38 0.74
## (4) author no stats backgroun - stats phd    4 10 3.80 1.21   4.00    4.00 1.11
## (5) story-first - data-first                 5 10 3.30 1.69   4.50    3.38 0.37
## (6) hate (rip up) - love (publish)           6 10 3.15 1.06   3.50    3.12 1.11
## (7) not clever - clever                      7 10 2.80 1.44   2.75    2.81 1.85
##                                           min max range  skew kurtosis   se
## (1) context-dependent - stand-alone         1 5.0   4.0 -0.45    -1.72 0.55
## (2) quick to make - long time to make       1 5.0   4.0 -0.05    -1.86 0.53
## (3) sole author - team author               1 5.0   4.0  0.37    -1.87 0.56
## (4) author no stats backgroun - stats phd   1 5.0   4.0 -1.07     0.16 0.38
## (5) story-first - data-first                1 5.0   4.0 -0.39    -1.91 0.53
## (6) hate (rip up) - love (publish)          2 4.5   2.5 -0.10    -2.00 0.33
## (7) not clever - clever                     1 4.5   3.5 -0.04    -1.93 0.45
## ARF TODO reformat as df to get ridgeplot of histograms w/ constructs as rows?

Describe Elements

# calculate descriptive statistics 
statsElements(p15)
## 
## ##################################
## Desriptive statistics for elements
## ##################################
## 
##                   vars n mean   sd median trimmed  mad min max range  skew
## (1) CAREBEAR_BARS    1 7 2.50 1.89      1    2.50 0.00 1.0 5.0   4.0  0.28
## (2) LADY_LINE        2 7 3.79 1.41      4    3.79 1.48 1.0 5.0   4.0 -0.92
## (3) BULLET_BARS      3 7 3.71 1.15      4    3.71 0.74 1.5 5.0   3.5 -0.80
## (4) CARTOMAP         4 7 2.86 1.11      3    2.86 1.48 1.5 4.5   3.0  0.23
## (5) TAXMAN_INFO      5 7 4.64 0.48      5    4.64 0.00 4.0 5.0   1.0 -0.47
## (6) PENGUIN_DISTS    6 7 3.71 1.22      4    3.71 0.74 2.0 5.0   3.0 -0.55
## (7) HISTO_DIST       7 7 2.07 1.54      1    2.07 0.00 1.0 4.5   3.5  0.65
## (8) IXN_EBARS        8 7 2.43 1.48      2    2.43 1.48 1.0 4.5   3.5  0.51
## (9) IXN_SLOPE        9 7 2.57 1.69      2    2.57 1.48 1.0 4.5   3.5  0.16
## (10) BAYES_RIDGES   10 7 3.00 1.55      3    3.00 2.22 1.0 5.0   4.0 -0.17
##                   kurtosis   se
## (1) CAREBEAR_BARS    -2.11 0.72
## (2) LADY_LINE        -0.64 0.53
## (3) BULLET_BARS      -0.76 0.43
## (4) CARTOMAP         -1.72 0.42
## (5) TAXMAN_INFO      -1.86 0.18
## (6) PENGUIN_DISTS    -1.66 0.46
## (7) HISTO_DIST       -1.63 0.58
## (8) IXN_EBARS        -1.67 0.56
## (9) IXN_SLOPE        -2.13 0.64
## (10) BAYES_RIDGES    -1.70 0.59

Element Correlations

elementCor(p15)
## 
## ############################
## Correlation between elements
## ############################
## 
## Type of correlation:  Cohens's rc (invariant to scale reflection) 
## 
##                        1     2     3     4     5     6     7     8     9    10
## (1) CAREBEAR_BARS  1      0.08  0.38 -0.47 -0.32 -0.38 -0.23 -0.34  0.00 -0.34
## (2) LADY_LINE      2            0.77  0.41  0.52 -0.04 -0.94 -0.88 -0.72 -0.62
## (3) BULLET_BARS    3                 -0.08  0.46  0.02 -0.74 -0.69 -0.33 -0.43
## (4) CARTOMAP       4                        0.00  0.03 -0.20 -0.23 -0.21 -0.41
## (5) TAXMAN_INFO    5                              0.53 -0.57 -0.45 -0.26 -0.15
## (6) PENGUIN_DISTS  6                                    0.14  0.27  0.41  0.43
## (7) HISTO_DIST     7                                          0.95  0.76  0.66
## (8) IXN_EBARS      8                                                0.68  0.85
## (9) IXN_SLOPE      9                                                      0.38
## (10) BAYES_RIDGES 10